A KNN-based model for non-invasive prediction of hemorrhagic shock severity in prehospital settings: integrating MAP, PBUCO2, PTCO2, and PPV
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Le résumé fourni par la source
Rapid prehospital assessment of hemorrhagic shock severity is critical for trauma triage and intervention. Current non-invasive single-parameter monitoring shows limited diagnostic reliability. We developed a multi-parameter predictive model integrating mean arterial pressure (MAP), buccal mucosal CO₂ (P BU CO₂), transcutaneous oxygen (P TC O₂), and pulse pressure variation (PPV). using K-nearest neighbors (KNN) algorithm. Forty-five Wistar rats were randomly divided into five groups ( n = 9) with different blood loss amounts. MAP, P BU CO 2 , P TC O 2 , and PPV measurements were continuously obtained. A multi-parameter shock severity prediction model was established based on the KNN algorithm. Leave-one-out cross-validation was used to determine the value of K . Meanwhile, a prediction model based on the support vector machine (SVM) algorithm was established. The performance of the two prediction models was compared using confusion matrices and receiver operating characteristic (ROC) curve. When the training vs testing data set ratio is 7:3 or 6:4, and K = 3, the KNN-based model has the best prediction accuracy (94.82% and 93.51%). The confusion matrix and ROC evaluation showed that the overall performance of the KNN-based model is superior to that of the SVM-based model, at all levels of blood loss (F1 = 95.09% and 93.99%, AUC = 1 and 0.97 for the KNN-based model at 7:3 and 6:4 dataset ratio; F1 = 83.84% and 84.86%, AUC = 0.97 and 0.97 for the SVM-based model at 7:3 and 6:4 dataset ratio). Using the detection indicators MAP, P BU CO 2 , P TC O 2 and PPV, the KNN-based rat hemorrhagic shock severity prediction model has high accuracy and stability, and demonstrates potential feasibility for severity stratification of hemorrhagic shock in standardized preclinical models, providing a foundation for future clinical validation in prehospital environments.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- A KNN-based model for non-invasive prediction of hemorrhagic shock severity in prehospital settings: integrating MAP, PBUCO2, PTCO2, and PPV
- Date Crossref
- 20/05/2025
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Union Hospital pays non établi dans la noticeÉtablissement de santé
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Army Medical University Department of Medical Engineering pays non établi dans la noticeUniversité ou école supérieure
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Xinqiao Hospital pays non établi dans la noticeÉtablissement de santé
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Chongqing University Key Laboratory of Optoelectronic Technology & Systems pays non établi dans la noticeUniversité ou école supérieure
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Ministry of Education pays non établi dans la noticeOrganisme public
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Chongqing University of Technology pays non établi dans la noticeUniversité ou école supérieure
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Daping Hospital pays non établi dans la noticeÉtablissement de santé
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Tongji Medical College Chongqing Hospital pays non établi dans la noticeUniversité ou école supérieure
Union Hospital, Department of Medical Engineering — Army Medical University et Xinqiao Hospital, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.